更改

此词条暂由Henry翻译。{{Refimprove|date=November 2008}}

此词条暂由Henry翻译。{{Refimprove|date=November 2008}}

 

An adaptive system is a set of interacting or interdependent entities, real or abstract, forming an integrated whole that together are able to respond to environmental changes or changes in the interacting parts, in a way analogous to either continuous physiological homeostasis or evolutionary adaptation in biology. Feedback loops represent a key feature of adaptive systems, such as ecosystems and individual organisms; or in the human world, communities, organizations, and families.

An adaptive system is a set of interacting or interdependent entities, real or abstract, forming an integrated whole that together are able to respond to environmental changes or changes in the interacting parts, in a way analogous to either continuous physiological homeostasis or evolutionary adaptation in biology. Feedback loops represent a key feature of adaptive systems, such as ecosystems and individual organisms; or in the human world, communities, organizations, and families.

<font color="#ff8000"> 适应系统adaptive system</font>是一组相互作用或相互依存的实体，它们们或真实或抽象，形成一个能够共同响应环境变化或相互作用部分变化的完整的整体。类似于生物学中持续的生理稳态或进化适应。反馈循环代表了适应系统的一个关键特征，例如生态系统和个体有机体; 或者在人类世界、社区、组织和家庭中。

<font color="#ff8000"> 适应系统adaptive system</font>是一组相互作用或相互依存的实体，它们或真实或抽象，形成一个能够共同响应环境的变化或相互作用部分的变化的综合整体，类似于生物学中持续的生理稳态或进化适应。反馈循环代表了适应系统的一个关键特征，例如生态系统和个体有机体; 或者在人类世界中的社区、组织和家庭。

Artificial adaptive systems include robots with control systems that utilize negative feedback to maintain desired states.

Artificial adaptive systems include robots with control systems that utilize negative feedback to maintain desired states.

The law of adaptation can be stated informally as:  

The law of adaptation can be stated informally as:  

适应法可以非正式地阐述如下:

适应法则可以非正式地被阐释为:

{{quote|Every adaptive system converges to a state in which all kind of stimulation ceases.<ref>José Antonio Martín H., Javier de Lope and Darío Maravall: "Adaptation, Anticipation and Rationality in Natural and Artificial Systems: Computational Paradigms Mimicking Nature" Natural Computing, December, 2009. Vol. 8(4), pp. 757-775. [https://dx.doi.org/10.1007/s11047-008-9096-6 doi]</ref>}}

{{quote|Every adaptive system converges to a state in which all kind of stimulation ceases.<ref>José Antonio Martín H., Javier de Lope and Darío Maravall: "Adaptation, Anticipation and Rationality in Natural and Artificial Systems: Computational Paradigms Mimicking Nature" Natural Computing, December, 2009. Vol. 8(4), pp. 757-775. [https://dx.doi.org/10.1007/s11047-008-9096-6 doi]</ref>}}

Formally, the law can be defined as follows:

Formally, the law can be defined as follows:

形式上，该法可以定义如下:

正式地，该法则可以定义如下:

Given a system <math>S</math>, we say that a physical event <math>E</math> is a stimulus for the system <math>S</math> if and only if the probability <math>P(S \rightarrow S'|E)</math> that the system suffers a change or be perturbed (in its elements or in its processes) when the event <math>E</math> occurs is strictly greater than the prior probability that <math>S</math> suffers a change independently of <math>E</math>:

Given a system <math>S</math>, we say that a physical event <math>E</math> is a stimulus for the system <math>S</math> if and only if the probability <math>P(S \rightarrow S'|E)</math> that the system suffers a change or be perturbed (in its elements or in its processes) when the event <math>E</math> occurs is strictly greater than the prior probability that <math>S</math> suffers a change independently of <math>E</math>:

给定一个系统数学 s / math，我们说物理事件数学 e / math 是系统数学 s / math 的刺激，当且仅当事件数学 e / math 发生时，系统遭受变化或受到扰动的概率数学 p (s  right tarrow s’ | e) / math 严格大于数学 s / math 独立于数学 e / math 遭受变化的先验概率:

给定一个系统<math>S</math>，我们说物理事件 <math>E</math>是<math>S</math>的刺激物，当且仅当数学<math>P(S \rightarrow S'|E)</math>，即当事件<math>E</math>发生时系统发生变化或（其组成元素或过程）被干扰的概率严格大于<math>S</math>独立于<math>E</math>而发生改变的概率。

 

<math>P(S \rightarrow S'|E)>P(S \rightarrow S') </math>

<math>P(S \rightarrow S'|E)>P(S \rightarrow S') </math>

Let <math>S</math> be an arbitrary system subject to changes in time <math>t</math> and let <math>E</math> be an arbitrary event that is a stimulus for the system <math>S</math>: we say that <math>S</math> is an adaptive system if and only if when t tends to infinity <math>(t\rightarrow \infty)</math> the probability that the system <math>S</math> change its behavior <math>(S\rightarrow S')</math> in a time step <math>t_0</math> given the event <math>E</math> is equal to the probability that the system change its behavior independently of the occurrence of the event <math>E</math>. In mathematical terms:

Let <math>S</math> be an arbitrary system subject to changes in time <math>t</math> and let <math>E</math> be an arbitrary event that is a stimulus for the system <math>S</math>: we say that <math>S</math> is an adaptive system if and only if when t tends to infinity <math>(t\rightarrow \infty)</math> the probability that the system <math>S</math> change its behavior <math>(S\rightarrow S')</math> in a time step <math>t_0</math> given the event <math>E</math> is equal to the probability that the system change its behavior independently of the occurrence of the event <math>E</math>. In mathematical terms:

假设s是一个随时间变化的任意系统，t是一个随时间变化的系统，e是一个随时间变化的事件，是系统s的一个刺激: 我们说s是一个适应性系统，当且仅当 t 趋向于无穷时(t→∞)，给定事件E的系统s在时间步长t0改变其行为（S→S′）的概率等于系统改变其行为独立于事件的发生的概率，用数学术语来表达如下：

假设<math>S</math>是一个随时间变化的任意系统，<math>t</math>是一个随时间变化的系统，<math>e</math>是一个随时间变化的事件，是系统<math>s</math>的一个刺激: 我们说<math>S</math>是一个适应性系统，当且仅当 t 趋向于无穷时(t→∞)，给定事件E的系统<math>S</math>在时间步长t0改变其行为（S→S′）的概率等于系统改变其行为独立于事件的发生的概率，用数学术语来表达如下：

- <math> P_{t_0}(S\rightarrow S'|E) > P_{t_0}(S\rightarrow S') > 0 </math>

- <math> P_{t_0}(S\rightarrow S'|E) > P_{t_0}(S\rightarrow S') > 0 </math>

#- <math> \lim_{t\rightarrow \infty} P_t(S\rightarrow S' | E) = P_t(S\rightarrow S')</math>

#- <math> \lim_{t\rightarrow \infty} P_t(S\rightarrow S' | E) = P_t(S\rightarrow S')</math>

- <math> \lim_{t\rightarrow \infty} P_t(S\rightarrow S' | E) = P_t(S\rightarrow S')</math>

- <math> \lim_{t\rightarrow \infty} P_t(S\rightarrow S' | E) = P_t(S\rightarrow S')</math>

Thus, for each instant <math>t</math> will exist a temporal interval <math>h</math> such that:

Thus, for each instant <math>t</math> will exist a temporal interval <math>h</math> such that:

因此，对于每一个即时t，都会存在一个时间间隔h，这样:

因此，对于每一个即时<math>t</math>，都会存在一个时间间隔<math>h</math>，这样:

<math> P_{t+h}(S\rightarrow S' | E) - P_{t+h}(S\rightarrow S') < P_t(S\rightarrow S' | E) - P_t(S\rightarrow S')</math>

<math> P_{t+h}(S\rightarrow S' | E) - P_{t+h}(S\rightarrow S') < P_t(S\rightarrow S' | E) - P_t(S\rightarrow S')</math>

In an adaptive system, a parameter changes slowly and has no preferred value.  In a self-adjusting system though, the parameter value “depends on the history of the system dynamics”.  One of the most important qualities of self-adjusting systems is its “adaptation to the edge of chaos” or ability to avoid chaos.  Practically speaking, by heading to the edge of chaos without going further, a leader may act spontaneously yet without disaster.  A March/April 2009 Complexity article further explains the self-adjusting systems used and the realistic implications. Physicists have shown that adaptation to the edge of chaos occurs in almost all systems with feedback.

In an adaptive system, a parameter changes slowly and has no preferred value.  In a self-adjusting system though, the parameter value “depends on the history of the system dynamics”.  One of the most important qualities of self-adjusting systems is its “adaptation to the edge of chaos” or ability to avoid chaos.  Practically speaking, by heading to the edge of chaos without going further, a leader may act spontaneously yet without disaster.  A March/April 2009 Complexity article further explains the self-adjusting systems used and the realistic implications. Physicists have shown that adaptation to the edge of chaos occurs in almost all systems with feedback.

在自适应系统中，参数变化缓慢，没有优先值。然而，在一个自调整系统中，参数值“取决于系统动力的历史”。自调节系统最重要的特性之一是它能“适应混沌的边缘”或避免混沌的能力。实际上，如果一个领导者走向混乱的边缘而不走得更远，那么他就可以在没有灾难的情况下自发地行动。2009年3/4月的一篇文章进一步解释了自我调节系统的使用和现实意义。物理学家已经证明，对混沌边缘的适应几乎发生在所有具有反馈的系统中。

在自适应系统中，参数变化缓慢，没有优先值。然而，在一个自调整系统中，参数值“取决于系统动力的历史”。自调节系统最重要的特性之一是它能“适应混乱的边缘”或避免混乱的能力。实际上来讲，通过朝着混乱的边缘出发但是不走的太远，领导者就可以在避免灾难的情况下自发地行动。Complexity期刊2009年3/4月一期的一篇文章进一步解释了自我调节系统的使用和现实意义。物理学家已经证明，对混沌边缘的适应几乎发生在所有具有反馈的系统中。

How do various types of adaptations interact in a living system? Practopoiesis, a term due to its originator Danko Nikolić, is a reference to a hierarchy of adaptation mechanisms answering this question. The adaptive hierarchy forms a kind of a self-adjusting system in which autopoiesis of the entire organism or a cell occurs through a hierarchy of allopoietic interactions among components. This is possible because the components are organized into a poietic hierarchy: adaptive actions of one component result in creation of another component. The theory proposes that living systems exhibit a hierarchy of a total of four such adaptive poietic operations:

How do various types of adaptations interact in a living system? Practopoiesis, a term due to its originator Danko Nikolić, is a reference to a hierarchy of adaptation mechanisms answering this question. The adaptive hierarchy forms a kind of a self-adjusting system in which autopoiesis of the entire organism or a cell occurs through a hierarchy of allopoietic interactions among components. This is possible because the components are organized into a poietic hierarchy: adaptive actions of one component result in creation of another component. The theory proposes that living systems exhibit a hierarchy of a total of four such adaptive poietic operations:

在一个生命系统中，各种类型的适应性是如何相互作用的？<font color="#ff8000"> 拓扑实践Practopoiesis</font>，这个术语源于它的发明者 Danko nikoli，是对回答这个问题的适应机制层次的一个参考。这种适应性层次结构形成了一种自我调节系统，其中整个生物体或细胞的自创生是通过各组分之间的异体生成相互作用而发生的。这是可能的，因为组件被组织成一个极端层次结构: 一个组件的自适应操作导致另一个组件的创建。该理论提出，生命系统展示了一个由四个这样的适应性极化操作组成的等级体系:

在一个生命系统中，各种类型的适应性是如何相互作用的？<font color="#ff8000"> 拓扑实践Practopoiesis</font>，这个术语源于它的发明者 Danko nikoli，指向了能回答这个问题的一个适应机制层次结构。这种适应性层次结构形成了一种自我调节系统，其中整个生物体或细胞的自创生是通过各组分之间的异体生成相互作用而发生的。这之所以可能是因为组件被组织成一个极端层次结构: 一个组件的自适应操作导致另一个组件的创建。该理论提出，生命系统展示了一个由四个这样的适应性极化操作组成的层级结构:

     evolution (i) &rarr; gene expression (ii) &rarr; non gene-involving homeostatic mechanisms (anapoiesis) (iii) &rarr; final cell function (iv)

     evolution (i) &rarr; gene expression (ii) &rarr; non gene-involving homeostatic mechanisms (anapoiesis) (iii) &rarr; final cell function (iv)

进化(i) ; 基因表达(ii) ; 非基因参与的稳态机制(anapoiesis) ; (iii) ; 最终细胞功能(iv)

进化(i) &rarr; 基因表达(ii) &rarr; 非基因参与的稳态机制(anapoiesis) (iii) &rarr; 最终细胞功能(iv)

As the hierarchy evolves towards higher levels of organization, the speed of adaptation increases. Evolution is the slowest; the final cell function is the fastest. Ultimately, practopoiesis challenges current neuroscience doctrine by asserting that mental operations primarily occur at the homeostatic, anapoietic level (iii) &mdash; i.e., that minds and thought emerge from fast homeostatic mechanisms poietically controlling the cell function. This contrasts the widespread belief that thinking is synonymous with neural activity (i.e., with the 'final cell function' at level iv).  

As the hierarchy evolves towards higher levels of organization, the speed of adaptation increases. Evolution is the slowest; the final cell function is the fastest. Ultimately, practopoiesis challenges current neuroscience doctrine by asserting that mental operations primarily occur at the homeostatic, anapoietic level (iii) &mdash; i.e., that minds and thought emerge from fast homeostatic mechanisms poietically controlling the cell function. This contrasts the widespread belief that thinking is synonymous with neural activity (i.e., with the 'final cell function' at level iv).  

随着等级制度向更高层次的组织发展，适应的速度也在加快。进化是最慢的，最后的细胞功能是最快的。最终，实践拓扑学挑战当前的神经科学学说，认为心理活动主要发生在稳态，非生物水平上。也就是说，头脑和思想从快速的稳态机制中产生，从而控制了细胞功能。这与人们普遍认为的思考是神经活动的同义词(也就是说，与第四级的“最终细胞功能”)形成了鲜明对比。

随着层级结构向更高级别的组织发展，适应的速度也在加快。进化是最慢的，最后的细胞功能是最快的。最终，实践拓扑学挑战当前的神经科学学说，认为心理活动主要发生在稳态，非生物水平上。也就是说，意识和想法从快速的稳态机制中产生，从而控制了细胞功能。这与人们普遍认为的思考是神经活动的同义词(也就是说，与第四级的“最终细胞功能”)形成了鲜明对比。

Each slower level contains knowledge that is more general than the faster level; for example, genes contain more general knowledge than anapoietic mechanisms, which in turn contain more general knowledge than cell functions. This hierarchy of knowledge enables the anapoietic level to directly activate concepts, which are the most fundamental ingredient for the emergence of the mind.

Each slower level contains knowledge that is more general than the faster level; for example, genes contain more general knowledge than anapoietic mechanisms, which in turn contain more general knowledge than cell functions. This hierarchy of knowledge enables the anapoietic level to directly activate concepts, which are the most fundamental ingredient for the emergence of the mind.

每一个较慢的层次包含的知识比较快的层次包含的知识更普遍; 例如，基因包含的一般知识比无生殖机制多，而无生殖机制又比细胞功能包含更多的一般知识。这种知识的层次结构使得无生命层次能够直接激活概念，而这些概念是头脑出现的最基本要素。

每一个较慢的层次包含的知识比较快的层次包含的知识更一般性; 例如，基因包含的一般知识比无生殖机制多，而无生殖机制又比细胞功能包含更多的一般知识。这种知识的层次结构使得无生命层次能够直接激活概念，而这些概念是意识出现的最基本的原料。

   | last = Martin H. | first = Jose Antonio. | authorlink = Jose Antonio Martin H.

   | last = Martin H. | first = Jose Antonio. | authorlink = Jose Antonio Martin H.

   | author2 = [[Javier de Lope]]; [[Darío Maravall]]

   | author2 = [[Javier de Lope]]; [[Darío Maravall]]